COVID-19

• Casos acumulados por país
  • Evolución Diaria
  • Evolución por día del primer caso detectado en el país
• Decesos acumulados por país
• Argentina
  • Curva de casos acumulados por día
  • Casos detectados por día

from datetime import datetime

currentSecond= datetime.now().second
currentMinute = datetime.now().minute
currentHour = datetime.now().hour

currentDay = datetime.now().day
currentMonth = datetime.now().month
currentYear = datetime.now().year

import pandas as pd
import xlrd

url = "https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide-"+str(currentYear)+"-0"+str(currentMonth)+"-"+str(currentDay)+".xlsx"
#df = pd.read_csv(url)
data = pd.read_excel(url)

data.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018
0	2020-03-31	31	3	2020	27	0	Afghanistan	AF	AFG	37172386.0
1	2020-03-30	30	3	2020	8	1	Afghanistan	AF	AFG	37172386.0
2	2020-03-29	29	3	2020	15	1	Afghanistan	AF	AFG	37172386.0
3	2020-03-28	28	3	2020	16	1	Afghanistan	AF	AFG	37172386.0
4	2020-03-27	27	3	2020	0	0	Afghanistan	AF	AFG	37172386.0

data['dateRep'].max()

Timestamp('2020-03-31 00:00:00')

data.dtypes

dateRep                    datetime64[ns]
day                                 int64
month                               int64
year                                int64
cases                               int64
deaths                              int64
countriesAndTerritories            object
geoId                              object
countryterritoryCode               object
popData2018                       float64
dtype: object

import numpy as np

data['dateRep']= pd.to_datetime(data['dateRep']) 
#data['dateRep']= pd.Series(data['dateRep'])

data_min = data[data.cases > 0].reset_index(drop=True)

data_min.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018
0	2020-03-31	31	3	2020	27	0	Afghanistan	AF	AFG	37172386.0
1	2020-03-30	30	3	2020	8	1	Afghanistan	AF	AFG	37172386.0
2	2020-03-29	29	3	2020	15	1	Afghanistan	AF	AFG	37172386.0
3	2020-03-28	28	3	2020	16	1	Afghanistan	AF	AFG	37172386.0
4	2020-03-26	26	3	2020	33	0	Afghanistan	AF	AFG	37172386.0

min_date = data_min.groupby(['countriesAndTerritories'], as_index=False)["dateRep"].min()
min_date = min_date.rename(columns={'dateRep': 'min_date'})
min_date.head()

	countriesAndTerritories	min_date
0	Afghanistan	2020-02-25
1	Albania	2020-03-09
2	Algeria	2020-02-26
3	Andorra	2020-03-03
4	Angola	2020-03-22

df = data.merge(min_date, on='countriesAndTerritories', how='left')
df.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018	min_date
0	2020-03-31	31	3	2020	27	0	Afghanistan	AF	AFG	37172386.0	2020-02-25
1	2020-03-30	30	3	2020	8	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
2	2020-03-29	29	3	2020	15	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
3	2020-03-28	28	3	2020	16	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
4	2020-03-27	27	3	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25

#df['DateRep']= pd.to_datetime(df['DateRep']) 
df['min_date']= pd.to_datetime(df['min_date']) 
df.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018	min_date
0	2020-03-31	31	3	2020	27	0	Afghanistan	AF	AFG	37172386.0	2020-02-25
1	2020-03-30	30	3	2020	8	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
2	2020-03-29	29	3	2020	15	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
3	2020-03-28	28	3	2020	16	1	Afghanistan	AF	AFG	37172386.0	2020-02-25
4	2020-03-27	27	3	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25

df['days'] = (df['dateRep'] - df['min_date']) / np.timedelta64(1, 'D')
df.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018	min_date	days
0	2020-03-31	31	3	2020	27	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	35.0
1	2020-03-30	30	3	2020	8	1	Afghanistan	AF	AFG	37172386.0	2020-02-25	34.0
2	2020-03-29	29	3	2020	15	1	Afghanistan	AF	AFG	37172386.0	2020-02-25	33.0
3	2020-03-28	28	3	2020	16	1	Afghanistan	AF	AFG	37172386.0	2020-02-25	32.0
4	2020-03-27	27	3	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	31.0

df.sort_values(by=['countriesAndTerritories', 'dateRep'], inplace=True)
df.head()

	dateRep	day	month	year	cases	deaths	countriesAndTerritories	geoId	countryterritoryCode	popData2018	min_date	days
81	2019-12-31	31	12	2019	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	-56.0
80	2020-01-01	1	1	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	-55.0
79	2020-01-02	2	1	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	-54.0
78	2020-01-03	3	1	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	-53.0
77	2020-01-04	4	1	2020	0	0	Afghanistan	AF	AFG	37172386.0	2020-02-25	-52.0

#df = pd.read_csv(StringIO(data))
cumsums = df.groupby(['countriesAndTerritories', 'dateRep'])["cases"].sum().fillna(0).groupby(level=0).cumsum()
df.set_index(['countriesAndTerritories', 'dateRep'], inplace=True)
df['cum_cases'] = cumsums
df.reset_index(inplace=True)
df.head(100)

	countriesAndTerritories	dateRep	day	month	year	cases	deaths	geoId	countryterritoryCode	popData2018	min_date	days	cum_cases
0	Afghanistan	2019-12-31	31	12	2019	0	0	AF	AFG	37172386.0	2020-02-25	-56.0	0
1	Afghanistan	2020-01-01	1	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-55.0	0
2	Afghanistan	2020-01-02	2	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-54.0	0
3	Afghanistan	2020-01-03	3	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-53.0	0
4	Afghanistan	2020-01-04	4	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-52.0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	Albania	2020-03-22	22	3	2020	6	0	AL	ALB	2866376.0	2020-03-09	13.0	76
96	Albania	2020-03-23	23	3	2020	13	0	AL	ALB	2866376.0	2020-03-09	14.0	89
97	Albania	2020-03-24	24	3	2020	11	2	AL	ALB	2866376.0	2020-03-09	15.0	100
98	Albania	2020-03-25	25	3	2020	23	1	AL	ALB	2866376.0	2020-03-09	16.0	123
99	Albania	2020-03-26	26	3	2020	23	0	AL	ALB	2866376.0	2020-03-09	17.0	146

100 rows × 13 columns

cumsums = df.groupby(['countriesAndTerritories', 'dateRep'])["deaths"].sum().fillna(0).groupby(level=0).cumsum()
df.set_index(['countriesAndTerritories', 'dateRep'], inplace=True)
df['cum_deaths'] = cumsums
df.reset_index(inplace=True)
df.head(100)

	countriesAndTerritories	dateRep	day	month	year	cases	deaths	geoId	countryterritoryCode	popData2018	min_date	days	cum_cases	cum_deaths
0	Afghanistan	2019-12-31	31	12	2019	0	0	AF	AFG	37172386.0	2020-02-25	-56.0	0	0
1	Afghanistan	2020-01-01	1	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-55.0	0	0
2	Afghanistan	2020-01-02	2	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-54.0	0	0
3	Afghanistan	2020-01-03	3	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-53.0	0	0
4	Afghanistan	2020-01-04	4	1	2020	0	0	AF	AFG	37172386.0	2020-02-25	-52.0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	Albania	2020-03-22	22	3	2020	6	0	AL	ALB	2866376.0	2020-03-09	13.0	76	2
96	Albania	2020-03-23	23	3	2020	13	0	AL	ALB	2866376.0	2020-03-09	14.0	89	2
97	Albania	2020-03-24	24	3	2020	11	2	AL	ALB	2866376.0	2020-03-09	15.0	100	4
98	Albania	2020-03-25	25	3	2020	23	1	AL	ALB	2866376.0	2020-03-09	16.0	123	5
99	Albania	2020-03-26	26	3	2020	23	0	AL	ALB	2866376.0	2020-03-09	17.0	146	5

100 rows × 14 columns

df['date_int'] = (df['year'] * 10000 + df['month'] * 100 + df['day']).astype(int)
df = df.rename(columns={'countriesAndTerritories': 'pais'})
df = df.sort_values(by=['date_int', 'pais'])
max_cases = df.groupby(['pais'], as_index=False)["cum_cases"].max()
max_cases = max_cases.rename(columns={'cum_cases': 'max_cases'})
df = df.merge(max_cases, on='pais', how='left')
df.head()

	pais	dateRep	day	month	year	cases	deaths	geoId	countryterritoryCode	popData2018	min_date	days	cum_cases	cum_deaths	date_int	max_cases
0	Afghanistan	2019-12-31	31	12	2019	0	0	AF	AFG	37172386.0	2020-02-25	-56.0	0	0	20191231	141
1	Algeria	2019-12-31	31	12	2019	0	0	DZ	DZA	42228429.0	2020-02-26	-57.0	0	0	20191231	511
2	Armenia	2019-12-31	31	12	2019	0	0	AM	ARM	2951776.0	2020-03-01	-61.0	0	0	20191231	482
3	Australia	2019-12-31	31	12	2019	0	0	AU	AUS	24992369.0	2020-01-25	-25.0	0	0	20191231	4557
4	Austria	2019-12-31	31	12	2019	0	0	AT	AUT	8847037.0	2020-02-26	-57.0	0	0	20191231	9618

df = df.sort_values(by=['date_int', 'geoId'])

df['days'].min()

0.0

df['dateRep'].max()

Timestamp('2020-03-31 00:00:00')

today = pd.to_datetime('today')

df = df.where(df['dateRep'] < today)
df = df.where(df['days'] >= 0)

df = df.dropna()

df.dtypes

pais                            object
dateRep                 datetime64[ns]
day                            float64
month                          float64
year                           float64
cases                          float64
deaths                         float64
geoId                           object
countryterritoryCode            object
popData2018                    float64
min_date                datetime64[ns]
days                           float64
cum_cases                      float64
cum_deaths                     float64
date_int                       float64
max_cases                      float64
dtype: object

import pandas as pd
import altair as alt

Casos acumulados por país

Evolución Diaria

# select a point for which to provide details-on-demand
source = df.where(df['max_cases'] >= 10000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('dateRep:T'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='dateRep:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=35, dy=-5, stroke='white', strokeWidth=5).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=35, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Evolución por día del primer caso detectado en el país

# select a point for which to provide details-on-demand
source = df.where(df['max_cases'] >= 1000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=40, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

source = df.where(df['cases'] > 0)
source = source.where(df['max_cases'] >= 30000)
alt.Chart(source).mark_line(point=True).encode(
    alt.X('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Y('cases:Q', scale=alt.Scale(type='log')),
    color='pais',
    tooltip=['pais:N', 'days:Q', 'cum_cases:Q']
).properties(
    width=700,
    height=400)

# select a point for which to provide details-on-demand
source = df.where(df['cases'] > 0)
source = source.where(df['max_cases'] >= 30000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line(point=True).encode(
    alt.X('cum_cases:Q', axis=alt.Axis(grid=False), scale=alt.Scale(type='log')),
    alt.Y('cases:Q', axis=alt.Axis(grid=False), scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='cum_cases:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=40, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Decesos acumulados por país

# select a point for which to provide details-on-demand
source = df#.where(df['max_cases'] >= 1000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('dateRep:T'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='dateRep:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='right', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='right', dx=5, dy=-15, stroke='white', strokeWidth=5).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='right', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='right', dx=5, dy=-15).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
).interactive()

# select a point for which to provide details-on-demand
source = df#.where(df['max_cases'] >= 1000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5, stroke='white', strokeWidth=2).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

date = data['dateRep'].max()
source = df.where(df['dateRep'] >= date)
source = source.where(source['max_cases'] >= 1000)

alt.Chart(source).mark_circle(size=100).encode(
    alt.X('cum_cases:Q'),
    alt.Y('cum_deaths:Q', scale=alt.Scale(type='log')),
    color='pais',
    tooltip=['pais:N', 'cum_deaths:Q', 'cum_cases:Q']
).properties(
    width=700,
    height=400
).interactive()

source = source.where(source['max_cases'] >= 1000)

alt.Chart(source).mark_circle(size=100).encode(
    alt.X('cum_cases:Q'),
    alt.Y('cum_deaths:Q'),
    color='pais',
    tooltip=['pais:N', 'cum_deaths:Q', 'cum_cases:Q']
).properties(
    width=700,
    height=400
).interactive()

Argentina

Curva de casos acumulados por día

# select a point for which to provide details-on-demand
source = df.where(df['pais'] == 'Argentina')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

# select a point for which to provide details-on-demand
source = df.where(df['pais'] == 'Argentina')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Casos detectados por día

source = df.where(df['pais'] == 'Argentina')

alt.Chart(source).mark_line(point=True).encode(
    alt.X('dateRep:T'),
    alt.Y('cases:Q', scale=alt.Scale(type='log')),
    color='pais',
    tooltip=['pais:N', 'dateRep:T', 'cum_cases:Q']
).properties(
    width=700,
    height=400)